ArticleNature communications2025
Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
4 citing papers in PubMed.
- Computational approaches for multimodal lineage tracing.Nature reviews. Genetics · 2026Review
- Lineage-aware stochastic modeling reveals gene-expression dynamics in development and disease.bioRxiv : the preprint server for biology · 2026Article
- The cell as a token: high-dimensional geometry in language models and cell embeddings.Bioinformatics (Oxford, England) · 2025Review
- Integrating representation learning, permutation, and optimization to detect lineage-related gene expression patterns.Nature communications · 2025Article
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2 authors.
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Abstract
Recent barcoding technologies allow reconstructing lineage trees while capturing paired single-cell RNA-sequencing (scRNA-seq) data. Such datasets provide opportunities to compare gene expression memory maintenance through lineage branching and pinpoint critical genes in these processes. Here we develop Permutation, Optimization, and Representation learning based single Cell gene Expression and Lineage ANalysis (PORCELAN) to identify lineage-informative genes or subtrees where lineage and expression are tightly coupled. We validate our method using synthetic data and apply it to recent paired lineage and scRNA-seq data of lung cancer in a mouse model and embryogenesis of mouse and C. elegans. Our method pinpoints subtrees giving rise to metastases or new cell states, and genes identified as most informative about lineage overlap with known pathways involved in lung cancer progression. Furthermore, our method highlights differences in how gene expression memory is maintained through divisions in cancer and embryogenesis, thereby providing a tool for studying cell state memory through divisions across biological systems.
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Registered trials
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